#2960

Easy

LeetCode Problem Workspace

Count Tested Devices After Test Operations

Simulate testing devices based on battery percentages to determine how many pass the test operations in sequence.

Problem Statement

You are given a 0-indexed integer array batteryPercentages with n elements, where each element represents the battery percentage of a device. Starting from the first device, you will perform test operations on each device in the array sequentially.

Each device can be tested if its battery percentage is greater than zero. After a successful test, its battery percentage decreases by one, and you count the device as tested. If the battery percentage is zero, you skip the test for that device and move on to the next. Return the number of devices that were successfully tested.

Examples

Example 1

Input: batteryPercentages = [1,1,2,1,3]

Output: 3

Performing the test operations in order starting from device 0: At device 0, batteryPercentages[0] > 0, so there is now 1 tested device, and batteryPercentages becomes [1,0,1,0,2]. At device 1, batteryPercentages[1] == 0, so we move to the next device without testing. At device 2, batteryPercentages[2] > 0, so there are now 2 tested devices, and batteryPercentages becomes [1,0,1,0,1]. At device 3, batteryPercentages[3] == 0, so we move to the next device without testing. At device 4, batteryPercentages[4] > 0, so there are now 3 tested devices, and batteryPercentages stays the same. So, the answer is 3.

Example 2

Input: batteryPercentages = [0,1,2]

Output: 2

Performing the test operations in order starting from device 0: At device 0, batteryPercentages[0] == 0, so we move to the next device without testing. At device 1, batteryPercentages[1] > 0, so there is now 1 tested device, and batteryPercentages becomes [0,1,1]. At device 2, batteryPercentages[2] > 0, so there are now 2 tested devices, and batteryPercentages stays the same. So, the answer is 2.

Constraints

1 <= n == batteryPercentages.length <= 100
0 <= batteryPercentages[i] <= 100

Solution Approach

Simulation of Test Operations

The most direct approach involves iterating through the batteryPercentages array. For each device, check if its battery percentage is greater than zero. If so, test the device and decrease its battery percentage. Track the number of devices tested.

O(n²) Approach (Naive Simulation)

In a brute-force method, for each device with a non-zero battery, simulate testing and decrementing the battery percentage. After testing one device, check if further testing is possible for others, which could lead to O(n²) time complexity.

Optimized Approach (Greedy Simulation)

By using a greedy approach, we can process each device efficiently. As we loop through the array, we can test a device and reduce its battery percentage without needing to check each device repeatedly, resulting in better performance in most cases.

Complexity Analysis

Metric	Value
Time	Depends on the final approach
Space	Depends on the final approach

The time complexity depends on the approach used. A naive simulation might have O(n²) complexity, especially if each device requires further checking of other devices. The space complexity is O(1), as we only need to keep track of the number of tested devices and modify the array in place.

What Interviewers Usually Probe

Look for the candidate's ability to implement sequential testing and handle array manipulations.
Evaluate their understanding of simulation techniques in algorithm design.
Check how they approach optimizing the simulation for better time complexity.

Common Pitfalls or Variants

Common pitfalls

Overcomplicating the solution by attempting unnecessary optimizations for this simple problem.
Not handling the case where a device has zero battery properly, skipping it in the correct order.
Assuming there's an obvious faster solution without considering the inherent simulation nature of the task.

Follow-up variants

Change the condition for when a device is considered tested (e.g., after a certain battery threshold is reached).
Add more operations after testing, such as recharging devices or resetting battery percentages.
Simulate testing with additional variables or constraints, such as multiple battery-charging cycles.

FAQ

How do I simulate test operations for devices in this problem?

Start by iterating through the array, checking each device's battery percentage. Test devices with non-zero batteries and reduce their percentage after each test. Track the number of devices tested.

What is the time complexity of the brute-force solution?

The brute-force solution can run in O(n²) time complexity, as it may require checking multiple devices after each test operation.

Can this problem be optimized beyond a naive simulation approach?

Yes, by using a more efficient simulation, such as a greedy algorithm, you can reduce unnecessary checks and improve time performance.

How does GhostInterview help with this problem?

GhostInterview guides you in applying simulation techniques and optimizing them for efficiency, which is crucial for tackling this problem.

What are common mistakes in solving the Count Tested Devices problem?

Common mistakes include not correctly handling devices with zero battery, overcomplicating the solution with unnecessary optimizations, and not tracking the tested devices correctly.

terminal

Solution

Solution 1: Simulation

Assume that the current number of devices we have tested is $ans$. When testing a new device $i$, its remaining battery is $\max(0, batteryPercentages[i] - ans)$. If the remaining battery is greater than $0$, it means this device can be tested, and we need to increase $ans$ by $1$.

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class Solution:
    def countTestedDevices(self, batteryPercentages: List[int]) -> int:
        ans = 0
        for x in batteryPercentages:
            ans += x > ans
        return ans

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